Robust distributed decision-making in robot swarms: Exploiting a third truth state

Michael Crosscombe; Jonathan Lawry; Sabine Hauert; Martin Homer

doi:10.1109/IROS.2017.8206297

Robust distributed decision-making in robot swarms: Exploiting a third truth state

Michael Crosscombe, Jonathan Lawry, Sabine Hauert, Martin Homer

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

23 Citations (Scopus)

975 Downloads (Pure)

Abstract

In this paper, we investigate the best-of-n distributed decision problem in robot swarms. In this context, we compare the weighted voter model with a three-valued model that incorporates an intermediate belief state meaning either 'uncertain' or 'indifferent'. We focus particularly on the trade-off between speed of convergence to a shared belief, and robustness to the presence of unreliable individuals in the population. By means of both simulation and embodied experiments in real robot swarms of 400 Kilobots, we show that the three-valued model is much more robust than the weighted voter model, but with decreased speed of convergence.

Original language	English
Title of host publication	2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
Subtitle of host publication	Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4326-4332
Number of pages	7
ISBN (Electronic)	9781538626825
ISBN (Print)	9781538626832
DOIs	https://doi.org/10.1109/IROS.2017.8206297
Publication status	Published - Feb 2018

Publication series

Name
ISSN (Print)	2153-0866

Research Groups and Themes

Engineering Mathematics Research Group

Keywords

best-of-n
decision-making
distributed
decentralised
swarm
robotics
three-valued

Access to Document

10.1109/IROS.2017.8206297

Full-text PDF (accepted author manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at http://ieeexplore.ieee.org/document/8206297 . Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 12 MBLicence: Unspecified

Handle.net

Persistent link

Professor Sabine Hauert
- sabine.hauertbristol.acuk
- School of Engineering Mathematics and Technology - Professor of Swarm Engineering
- Cancer
Person: Academic , Member
Professor Jonathan Lawry
- J.Lawrybristol.acuk
Person: Academic , Member

Cite this

Crosscombe, M., Lawry, J., Hauert, S., & Homer, M. (2018). Robust distributed decision-making in robot swarms: Exploiting a third truth state. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017): Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada (pp. 4326-4332). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IROS.2017.8206297

Crosscombe, Michael ; Lawry, Jonathan ; Hauert, Sabine et al. / Robust distributed decision-making in robot swarms : Exploiting a third truth state. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017): Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2018. pp. 4326-4332

@inproceedings{3848d11af298499ab3507d88f7884f17,

title = "Robust distributed decision-making in robot swarms: Exploiting a third truth state",

abstract = "In this paper, we investigate the best-of-n distributed decision problem in robot swarms. In this context, we compare the weighted voter model with a three-valued model that incorporates an intermediate belief state meaning either 'uncertain' or 'indifferent'. We focus particularly on the trade-off between speed of convergence to a shared belief, and robustness to the presence of unreliable individuals in the population. By means of both simulation and embodied experiments in real robot swarms of 400 Kilobots, we show that the three-valued model is much more robust than the weighted voter model, but with decreased speed of convergence.",

keywords = "best-of-n, decision-making, distributed, decentralised, swarm, robotics, three-valued",

author = "Michael Crosscombe and Jonathan Lawry and Sabine Hauert and Martin Homer",

year = "2018",

month = feb,

doi = "10.1109/IROS.2017.8206297",

language = "English",

isbn = "9781538626832",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "4326--4332",

booktitle = "2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)",

address = "United States",

}

Crosscombe, M, Lawry, J , Hauert, S & Homer, M 2018, Robust distributed decision-making in robot swarms: Exploiting a third truth state. in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017): Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada. Institute of Electrical and Electronics Engineers (IEEE), pp. 4326-4332. https://doi.org/10.1109/IROS.2017.8206297

Robust distributed decision-making in robot swarms: Exploiting a third truth state. / Crosscombe, Michael; Lawry, Jonathan ; Hauert, Sabine et al.
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017): Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 4326-4332.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Robust distributed decision-making in robot swarms

T2 - Exploiting a third truth state

AU - Crosscombe, Michael

AU - Lawry, Jonathan

AU - Hauert, Sabine

AU - Homer, Martin

PY - 2018/2

Y1 - 2018/2

N2 - In this paper, we investigate the best-of-n distributed decision problem in robot swarms. In this context, we compare the weighted voter model with a three-valued model that incorporates an intermediate belief state meaning either 'uncertain' or 'indifferent'. We focus particularly on the trade-off between speed of convergence to a shared belief, and robustness to the presence of unreliable individuals in the population. By means of both simulation and embodied experiments in real robot swarms of 400 Kilobots, we show that the three-valued model is much more robust than the weighted voter model, but with decreased speed of convergence.

AB - In this paper, we investigate the best-of-n distributed decision problem in robot swarms. In this context, we compare the weighted voter model with a three-valued model that incorporates an intermediate belief state meaning either 'uncertain' or 'indifferent'. We focus particularly on the trade-off between speed of convergence to a shared belief, and robustness to the presence of unreliable individuals in the population. By means of both simulation and embodied experiments in real robot swarms of 400 Kilobots, we show that the three-valued model is much more robust than the weighted voter model, but with decreased speed of convergence.

KW - best-of-n

KW - decision-making

KW - distributed

KW - decentralised

KW - swarm

KW - robotics

KW - three-valued

U2 - 10.1109/IROS.2017.8206297

DO - 10.1109/IROS.2017.8206297

M3 - Conference Contribution (Conference Proceeding)

SN - 9781538626832

SP - 4326

EP - 4332

BT - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Crosscombe M, Lawry J , Hauert S , Homer M. Robust distributed decision-making in robot swarms: Exploiting a third truth state. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017): Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada. Institute of Electrical and Electronics Engineers (IEEE). 2018. p. 4326-4332 Epub 2017 Dec 14. doi: 10.1109/IROS.2017.8206297

Robust distributed decision-making in robot swarms: Exploiting a third truth state

Abstract

Publication series

Research Groups and Themes

Keywords

Access to Document

Handle.net

Fingerprint

Profiles

Professor Sabine Hauert

Professor Jonathan Lawry

Cite this